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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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19 pages, 7846 KiB  
Article
A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data
by Barbara Cardone, Ferdinando Di Martino, Cristiano Mauriello and Vittorio Miraglia
ISPRS Int. J. Geo-Inf. 2024, 13(11), 377; https://doi.org/10.3390/ijgi13110377 - 30 Oct 2024
Viewed by 1464
Abstract
This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land [...] Read more.
This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements. Full article
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29 pages, 38136 KiB  
Article
Constructing Efficient Mesh-Based Global Grid Systems with Reduced Distortions
by Lakin Wecker, John Hall and Faramarz F. Samavati
ISPRS Int. J. Geo-Inf. 2024, 13(11), 373; https://doi.org/10.3390/ijgi13110373 - 22 Oct 2024
Cited by 1 | Viewed by 1775
Abstract
Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial [...] Read more.
Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations. Full article
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36 pages, 13506 KiB  
Article
ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models
by Ali Mansourian and Rachid Oucheikh
ISPRS Int. J. Geo-Inf. 2024, 13(10), 348; https://doi.org/10.3390/ijgi13100348 - 1 Oct 2024
Cited by 6 | Viewed by 8886
Abstract
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to [...] Read more.
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. Full article
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24 pages, 210044 KiB  
Article
Scale- and Resolution-Adapted Shaded Relief Generation Using U-Net
by Marianna Farmakis-Serebryakova, Magnus Heitzler and Lorenz Hurni
ISPRS Int. J. Geo-Inf. 2024, 13(9), 326; https://doi.org/10.3390/ijgi13090326 - 12 Sep 2024
Cited by 1 | Viewed by 1659
Abstract
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) [...] Read more.
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) relates to the neural network process and the maps used for training. Currently, there is no clear guidance on which DEM resolution to use to generate relief shading at specific scales. To address this gap, we trained the U-Net models on swisstopo manual relief shadings of Switzerland at four different scales and using four different resolutions of SwissALTI3D DEM. An interactive web application designed for this study allows users to outline a random area and compare histograms of varying brightness between predictions and manual relief shadings. The results showed that DEM resolution and output scale influence the appearance of the relief shading, with an overall scale/resolution ratio. We present guidelines for generating relief shading with neural networks for arbitrary areas and scales. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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28 pages, 37910 KiB  
Article
Cultural Heritage in Times of Crisis: Damage Assessment in Urban Areas of Ukraine Using Sentinel-1 SAR Data
by Ute Bachmann-Gigl and Zahra Dabiri
ISPRS Int. J. Geo-Inf. 2024, 13(9), 319; https://doi.org/10.3390/ijgi13090319 - 5 Sep 2024
Cited by 2 | Viewed by 1927
Abstract
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the [...] Read more.
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the application of Earth observation (EO) synthetic aperture radar (SAR) technology, and in particular Sentinel-1 SAR coherence time-series analysis, to monitor spatial and temporal changes related to the recent Russian–Ukrainian war in the urban areas of Mariupol and Kharkiv, Ukraine. The study considers key events during the siege of Mariupol and the battle of Kharkiv from February to May 2022. Built-up areas and cultural property were identified using freely available OpenStreetMap (OSM) data. Semi-automated coherent change-detection technique (CCD) that utilize difference analysis of pre- and co-conflict coherences were capable of highlighting areas of major impact on the urban structures. The study applied a logistic regression model (LRM) for the discrimination of damaged and undamaged buildings based on an estimated likelihood of damage occurrence. A good agreement was observed with the reference data provided by the United Nations Satellite Centre (UNOSAT) in terms of the overall extent of damage. Damage maps enable the localization of buildings and cultural assets in areas with a high probability of damage and can serve as the basis for a high-resolution follow-up investigation. The study reveals the benefits of Sentinel-1 SAR CCD in the sense of unsupervised delineation of areas affected by armed conflict. However, limitations arise in the detection of local and single-building damage compared to regions with large-scale destruction. The proposed semi-automated multi-temporal Sentinel-1 data analysis using CCD methodology shows its applicability for the timely investigation of damage to buildings and cultural heritage, which can support the response to crises. Full article
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17 pages, 7654 KiB  
Article
The Impact of Airbnb on Long-Term Rental Markets in San Francisco: A Geospatial Analysis Using Multiscale Geographically Weighted Regression
by Dongkeun Hur, Seonjin Lee and Hany Kim
ISPRS Int. J. Geo-Inf. 2024, 13(9), 298; https://doi.org/10.3390/ijgi13090298 - 23 Aug 2024
Viewed by 2763
Abstract
The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap [...] Read more.
The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group. Full article
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27 pages, 20774 KiB  
Article
Genetic Programming to Optimize 3D Trajectories
by André Kotze, Moritz Jan Hildemann, Vítor Santos and Carlos Granell
ISPRS Int. J. Geo-Inf. 2024, 13(8), 295; https://doi.org/10.3390/ijgi13080295 - 20 Aug 2024
Viewed by 1860
Abstract
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal [...] Read more.
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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26 pages, 9857 KiB  
Article
Spatiotemporal Analysis of Nighttime Crimes in Vienna, Austria
by Jiyoung Lee, Michael Leitner and Gernot Paulus
ISPRS Int. J. Geo-Inf. 2024, 13(7), 247; https://doi.org/10.3390/ijgi13070247 - 10 Jul 2024
Cited by 1 | Viewed by 2715
Abstract
Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during [...] Read more.
Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. Full article
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20 pages, 8876 KiB  
Article
A Comprehensive Survey on High-Definition Map Generation and Maintenance
by Kaleab Taye Asrat and Hyung-Ju Cho
ISPRS Int. J. Geo-Inf. 2024, 13(7), 232; https://doi.org/10.3390/ijgi13070232 - 1 Jul 2024
Cited by 3 | Viewed by 3725
Abstract
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully [...] Read more.
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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19 pages, 6750 KiB  
Article
A Sensor-Based Simulation Method for Spatiotemporal Event Detection
by Yuqin Jiang, Andrey A. Popov, Zhenlong Li, Michael E. Hodgson and Binghu Huang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 141; https://doi.org/10.3390/ijgi13050141 - 23 Apr 2024
Cited by 2 | Viewed by 1897
Abstract
Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the [...] Read more.
Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as “sensors”, which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. Full article
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21 pages, 11156 KiB  
Article
Map Reading and Analysis with GPT-4V(ision)
by Jinwen Xu and Ran Tao
ISPRS Int. J. Geo-Inf. 2024, 13(4), 127; https://doi.org/10.3390/ijgi13040127 - 11 Apr 2024
Cited by 7 | Viewed by 4178
Abstract
In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geography, and spatial data science. In this study, we explore [...] Read more.
In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geography, and spatial data science. In this study, we explore reading and analyzing maps with the latest version of GPT-4-vision-preview (GPT-4V), to fully evaluate its advantages and disadvantages in comparison with human eye-based visual inspections. We found that GPT-4V is able to properly retrieve information from various types of maps in different scales and spatiotemporal resolutions. GPT-4V can also perform basic map analysis, such as identifying visual changes before and after a natural disaster. It has the potential to replace human efforts by examining batches of maps, accurately extracting information from maps, and linking observed patterns with its pre-trained large dataset. However, it is encumbered by limitations such as diminished accuracy in visual content extraction and a lack of validation. This paper sets an example of effectively using GPT-4V for map reading and analytical tasks, which is a promising application for large multimodal models, large language models, and artificial intelligence. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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36 pages, 2828 KiB  
Review
Framing VRRSability Relationships among Vulnerability, Risk, Resilience, and Sustainability for Improving Geo-Information Evaluations within Geodesign Decision Support
by Timothy Nyerges, John A. Gallo, Keith M. Reynolds, Steven D. Prager, Philip J. Murphy and Wenwen Li
ISPRS Int. J. Geo-Inf. 2024, 13(3), 67; https://doi.org/10.3390/ijgi13030067 - 23 Feb 2024
Viewed by 2391
Abstract
Improving geo-information decision evaluation is an important part of geospatial decision support research, particularly when considering vulnerability, risk, resilience, and sustainability (V-R-R-S) of urban land–water systems (ULWSs). Previous research enumerated a collection of V-R-R-S conceptual component commonalties and differences resulting in a synthesis [...] Read more.
Improving geo-information decision evaluation is an important part of geospatial decision support research, particularly when considering vulnerability, risk, resilience, and sustainability (V-R-R-S) of urban land–water systems (ULWSs). Previous research enumerated a collection of V-R-R-S conceptual component commonalties and differences resulting in a synthesis concept called VRRSability. As a single concept, VRRSability enhances our understanding of the relationships within and among V-R-R-S. This paper reports research that extends and deepens the VRRSability synthesis by elucidating relationships among the V-R-R-S concepts, and organizes them into a VRRSability conceptual framework meant to guide operationalization within decision support systems. The core relationship within the VRRSability framework is ‘functional performance’, which couples land and water concerns within complex ULWS. Using functional performance, we elucidate other significant conceptual relationships, e.g., scale, scenarios and social knowledge, among others. A narrative about the functional performance of green stormwater infrastructure as part of a ULWS offers a practical application of the conceptual framework. VRRSability decision evaluation trade-offs among land and water emerge through the narrative, particularly how land cover influences water flow, which in turn influences water quality. The discussion includes trade-offs along risk–resilience and vulnerability–sustainability dimensions as key aspects of functional performance. Conclusions include knowledge contributions about a VRRSability conceptual framework and the next steps for operationalization within decision support systems using artificial intelligence. Full article
(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)
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41 pages, 7235 KiB  
Article
Temporal Paths in Real-World Sensor Networks
by Erik Bollen, Bart Kuijpers, Valeria Soliani and Alejandro Vaisman
ISPRS Int. J. Geo-Inf. 2024, 13(2), 36; https://doi.org/10.3390/ijgi13020036 - 24 Jan 2024
Viewed by 2046
Abstract
Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and [...] Read more.
Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and analysis. Temporal graph data models, whose nodes contain time-series data reported by the sensors, have been proposed to model and analyze these networks in order to take informed and timely decisions on their operation. Temporal paths are first-class citizens in this model and some classes of them have been identified in the literature. Queries aimed at finding these paths are denoted as (temporal) path queries. In spite of these efforts, many interesting problems remain open and, in this work, we aim at answering some of them. More concretely, we characterize the classes of temporal paths that can be defined in a sensor network in terms of the well-known Allen’s temporal algebra. We also show that, out of the 8192 possible interval relations in this algebra, only 11 satisfy two desirable properties that we define: transitivity and robustness. We show how these properties and the paths that satisfy them are relevant in practice by means of a real-world use case consisting of an analysis of salinity that appears close to the Scheldt river in Flanders, Belgium, during high tides occurring in the North Sea. Full article
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33 pages, 17787 KiB  
Article
Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction
by Frédéric Leroux, Mickaël Germain, Étienne Clabaut, Yacine Bouroubi and Tony St-Pierre
ISPRS Int. J. Geo-Inf. 2024, 13(1), 20; https://doi.org/10.3390/ijgi13010020 - 7 Jan 2024
Cited by 2 | Viewed by 3299
Abstract
Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the [...] Read more.
Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the PicassoNet-II semantic segmentation architecture. Additionally, we integrate Markov field-based contextual analysis for post-segmentation assessment and cluster analysis algorithms for building instantiation. Training a model to adapt to diverse datasets necessitates a substantial volume of annotated data, encompassing both real data from Quebec City, Canada, and simulated data from Evermotion and Unreal Engine. The experimental results indicate that incorporating simulated data improves segmentation accuracy, especially for under-represented features, and the DBSCAN algorithm proves effective in extracting isolated buildings. We further show that the model is highly sensible for the method of creating 3D meshes. Full article
(This article belongs to the Special Issue Unlocking the Power of Geospatial Data: Semantic Information Extraction, Ontology Engineering, and Deep Learning for Knowledge Discovery)
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21 pages, 6474 KiB  
Article
Redesigning Graphical User Interface of Open-Source Geospatial Software in a Community-Driven Way: A Case Study of GRASS GIS
by Linda Karlovska, Anna Petrasova, Vaclav Petras and Martin Landa
ISPRS Int. J. Geo-Inf. 2023, 12(9), 376; https://doi.org/10.3390/ijgi12090376 - 10 Sep 2023
Viewed by 2966
Abstract
Learning to use geographic information system (GIS) software effectively may be intimidating due to the extensive range of features it offers. The GRASS GIS software, in particular, presents additional challenges for first-time users in terms of its complex startup procedure and unique terminology [...] Read more.
Learning to use geographic information system (GIS) software effectively may be intimidating due to the extensive range of features it offers. The GRASS GIS software, in particular, presents additional challenges for first-time users in terms of its complex startup procedure and unique terminology associated with its data structure. On the other hand, a substantial part of the GRASS user community including us as developers recognized and embraced the advantages of the current approach. Given the controversial nature of the whole issue, we decided to actively involve regular users by conducting several formal surveys and by performing usability testing. Throughout this process, we discovered that resolving specific software issues through pure user-centered design is not always feasible, particularly in the context of open-source scientific software where the boundary between users and developers is very fuzzy. To address this challenge, we adopted the user-centered methodology tailored to the requirements of open-source scientific software development, which we refer to as community-driven design. This paper describes the community-driven redesigning process on the GRASS GIS case study and sets a foundation for applying community-driven design in other open-source scientific projects by providing insights into effective software development practices driven by the needs and input of the project’s community. Full article
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15 pages, 8343 KiB  
Article
The Impacts of Public Schools on Housing Prices of Residential Properties: A Case Study of Greater Sydney, Australia
by Yi Lu, Vivien Shi and Christopher James Pettit
ISPRS Int. J. Geo-Inf. 2023, 12(7), 298; https://doi.org/10.3390/ijgi12070298 - 24 Jul 2023
Cited by 2 | Viewed by 3925
Abstract
Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this [...] Read more.
Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this gap, three types of valuation models were applied to sales data from both non-strata and strata properties, and how the proximity and quality of public schools have influenced the prices of different residential property types was examined. The findings demonstrate that an increase of one unit in the normalised NAPLAN score of primary and high schools will lead to a 3.9% and 1.4%, 2.7% and 2.8% rise in housing prices for non-strata and strata properties, respectively. It is also indicated that the application of geographically weighted regression (GWR) can better capture the varying effects of schools across space. Moreover, properties located in the catchment of high-scoring schools in northern Greater Sydney are consistently the most influenced by school quality, regardless of the property type. These findings contribute to a comprehensive understanding of the relationships between public schools and the various submarkets of Greater Sydney. This is valuable for the decision-making processes of home buyers, developers and policymakers. Full article
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13 pages, 5732 KiB  
Article
Mapping with ChatGPT
by Ran Tao and Jinwen Xu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 284; https://doi.org/10.3390/ijgi12070284 - 16 Jul 2023
Cited by 44 | Viewed by 18640
Abstract
The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. [...] Read more.
The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. We conducted a pilot study exploring making maps with ChatGPT, a popular artificial intelligence (AI) chatbot. Specifically, we tested designing thematic maps using given or public geospatial data, as well as creating mental maps purely using textual descriptions of geographic space. We conclude that ChatGPT provides a useful alternative solution for mapping given its unique advantages, such as lowering the barrier to producing maps, boosting the efficiency of massive map production, and understanding geographical space with its spatial thinking capability. However, mapping with ChatGPT still has limitations at the current stage, such as its unequal benefits for different users and dependence on user intervention for quality control. Full article
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11 pages, 2037 KiB  
Article
Exploring Spatial Mismatch between Primary Care and Older Populations in an Aging Country: A Case Study of South Korea
by Jeon-Young Kang, Sandy Wong, Jinwoo Park, Jinhyung Lee and Jared Aldstadt
ISPRS Int. J. Geo-Inf. 2023, 12(7), 255; https://doi.org/10.3390/ijgi12070255 - 22 Jun 2023
Cited by 4 | Viewed by 3389
Abstract
With the rapid growth of aging populations in South Korea, it is important to assess spatial accessibility to healthcare resources as older adults may need frequent visits to hospitals. Healthcare spatial accessibility is measured based on available resources (e.g., physicians, beds, services), demands [...] Read more.
With the rapid growth of aging populations in South Korea, it is important to assess spatial accessibility to healthcare resources as older adults may need frequent visits to hospitals. Healthcare spatial accessibility is measured based on available resources (e.g., physicians, beds, services), demands (e.g., population), and travel costs (e.g., distance or time). In this study, we employed an Enhanced Two-Step Floating Catchment Area (E2SFCA) method to measure the spatial accessibility to primary care for older populations (i.e., aged 65 and older) in major cities in South Korea, including Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan. We found that the aging population in Seoul, the capital and biggest city in South Korea, has relatively better accessibility than those living in other cities. We also discovered a negative relationship between accessibility to primary care and the aging index (i.e., population over 65 years old/population less than 15 years old); the regions with a higher ratio of older populations have lower accessibility to primary care. The results suggested that more primary care services (perhaps via mobile vans) are needed in regions predominantly with older people to improve their healthcare access. Full article
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22 pages, 3043 KiB  
Article
PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting
by Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
ISPRS Int. J. Geo-Inf. 2023, 12(6), 241; https://doi.org/10.3390/ijgi12060241 - 16 Jun 2023
Cited by 4 | Viewed by 2589
Abstract
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is [...] Read more.
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules. Specifically, we use a new spatiotemporal attention multi-graph convolution that can extract extensive and comprehensive dynamic spatial dependence between nodes, in which multiple graph convolutions adopt progressive connections and spatiotemporal attention dynamically adjusts each item of the Chebyshev polynomial in graph convolutions. In addition, multi-scale time convolution was added to obtain an extensive and comprehensive dynamic time dependence from multiple receptive field features. We used real datasets to predict traffic speed and traffic flow, and the results were compared with a variety of typical prediction models. PMGCN has the smallest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) results under different horizons (H = 15 min, 30 min, 60 min), which shows the superiority of the proposed model. Full article
(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)
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17 pages, 5740 KiB  
Article
A Dynamic Management and Integration Framework for Models in Landslide Early Warning System
by Liang Liu, Jiqiu Deng and Yu Tang
ISPRS Int. J. Geo-Inf. 2023, 12(5), 198; https://doi.org/10.3390/ijgi12050198 - 13 May 2023
Cited by 2 | Viewed by 2641
Abstract
The landslide early warning system (LEWS) relies on various models for data processing, prediction, forecasting, and warning level discrimination. The potential different programming implementations and dependencies of these models complicate the deployment and integration of LEWS. Moreover, the coupling between LEWS and models [...] Read more.
The landslide early warning system (LEWS) relies on various models for data processing, prediction, forecasting, and warning level discrimination. The potential different programming implementations and dependencies of these models complicate the deployment and integration of LEWS. Moreover, the coupling between LEWS and models makes it hard to modify or replace models rapidly and dynamically according to changes in business requirements (such as updating the early warning business process, adjusting the model parameters, etc.). This paper proposes a framework for dynamic management and integration of models in LEWS by using WebAPIs and Docker to standardize model interfaces and facilitate model deployment, using Kubernetes and Istio to enable microservice architecture, dynamic scaling, and high availability of models, and using a model repository management system to manage and orchestrate model-related information and application processes. The results of applying this framework to a real LEWS demonstrate that our approach can support efficient deployment, management, and integration of models within the system. Furthermore, it provides a rapid and feasible implementation method for upgrading, expanding, and maintaining LEWS in response to changes in business requirements. Full article
(This article belongs to the Topic Advances in Earth Observation and Geosciences)
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20 pages, 8792 KiB  
Article
Dominant Modes of Agricultural Production Helped Structure Initial COVID-19 Spread in the U.S. Midwest
by Luke Bergmann, Luis Fernando Chaves, David O’Sullivan and Robert G. Wallace
ISPRS Int. J. Geo-Inf. 2023, 12(5), 195; https://doi.org/10.3390/ijgi12050195 - 9 May 2023
Cited by 4 | Viewed by 4870
Abstract
The spread of COVID-19 is geographically uneven in agricultural regions. Explanations proposed include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. Here, we additionally explore the impacts of coexisting modes of agricultural production across counties from twelve [...] Read more.
The spread of COVID-19 is geographically uneven in agricultural regions. Explanations proposed include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. Here, we additionally explore the impacts of coexisting modes of agricultural production across counties from twelve midwestern U.S. states. In modeling COVID-19 spread before vaccine authorization, we employed and extended spatial statistical methods that make different assumptions about the natures and scales of underlying sociospatial processes. In the process, we also develop a novel approach to visualizing the results of geographically weighted regressions that allows us to identify distinctive regional regimes of epidemiological processes. Our approaches allowed for models using abstract spatial weights (e.g., inverse-squared distances) to be meaningfully improved by also integrating process-specific relations (e.g., the geographical relations of the food system or of commuting). We thus contribute in several ways to methods in health geography and epidemiology for identifying contextually sensitive public engagements in socio-eco-epidemiological issues. Our results further show that agricultural modes of production are associated with the spread of COVID-19, with counties more engaged in modes of regenerative agricultural production having lower COVID-19 rates than those dominated by modes of conventional agricultural production, even when accounting for other factors. Full article
(This article belongs to the Collection Spatial Components of COVID-19 Pandemic)
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23 pages, 11603 KiB  
Article
Assessment of Perceived and Physical Walkability Using Street View Images and Deep Learning Technology
by Youngok Kang, Jiyeon Kim, Jiyoung Park and Jiyoon Lee
ISPRS Int. J. Geo-Inf. 2023, 12(5), 186; https://doi.org/10.3390/ijgi12050186 - 2 May 2023
Cited by 21 | Viewed by 5605
Abstract
As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study is to visualize physical and perceived [...] Read more.
As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study is to visualize physical and perceived walkability in detail and analyze the differences to prepare alternatives for improving the neighborhood’s walking environment. The study area is Jeonju City, one of the medium-sized cities in Korea. For the evaluation of perceived walkability, 196,624 street view images were crawled and 127,317 pairs of training datasets were constructed. After developing a convolutional neural network model, the scores of perceived walkability are predicted. For the evaluation of physical walkability, eight indicators are selected, and the score of overall physical walkability is calculated by combining the scores of the eight indicators. After that, the scores of perceived and physical walkability are visualized, and the difference between them is analyzed. This study is novel in three aspects. First, we develop a deep learning model that can improve the accuracy of perceived walkability using street view images, even in small and medium-sized cities. Second, in analyzing the characteristics of street view images, the possibilities and limitations of the semantic segmentation technique are confirmed. Third, the differences between perceived and physical walkability are analyzed in detail, and how the results of our study can be used to prepare alternatives for improving the walking environment is presented. Full article
(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)
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21 pages, 10571 KiB  
Article
MAC-GAN: A Community Road Generation Model Combining Building Footprints and Pedestrian Trajectories
by Lin Yang, Jing Wei, Zejun Zuo and Shunping Zhou
ISPRS Int. J. Geo-Inf. 2023, 12(5), 181; https://doi.org/10.3390/ijgi12050181 - 25 Apr 2023
Cited by 2 | Viewed by 2479
Abstract
Community roads are crucial to community navigation. There are automatic methods to obtain community roads using trajectories, but the sparsity and uneven density distribution of community trajectories present significant challenges in identifying community roads. To overcome these challenges, we propose a conditional generative [...] Read more.
Community roads are crucial to community navigation. There are automatic methods to obtain community roads using trajectories, but the sparsity and uneven density distribution of community trajectories present significant challenges in identifying community roads. To overcome these challenges, we propose a conditional generative adversarial network (MAC-GAN) supervised by pedestrian trajectories and neighborhood building footprints for road generation. MAC-GAN packs the “road trajectory–building footprint” pairs into images to characterize implicit ternary relations and sets up a multi-scale skip-connected and asymmetric convolution-based generator to incorporate such a relationship, in which the generator and discriminator mutually learn to optimize the network parameters and then derive approximate optimal results. Experiments on 37 real-world community datasets in Wuhan, China, are conducted to verify the effectiveness of the proposed model. The experimental results show that the F1 score of our model increases by 1.7–6.8%, and the IOU of our model increases by 2.2–7.5% compared with three baselines (i.e., Pix2pix, GANmapper, and DLinkGAN (configured by DLinknet)). In areas with sparse and missing trajectory data, the generated fine roads have high accuracy with the supervision of building footprints. Full article
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21 pages, 13411 KiB  
Article
Identification and Spatiotemporal Analysis of Bikesharing-Metro Integration Cycling
by Hao Wu, Yanhui Wang, Yuqing Sun, Duoduo Yin, Zhanxing Li and Xiaoyue Luo
ISPRS Int. J. Geo-Inf. 2023, 12(4), 166; https://doi.org/10.3390/ijgi12040166 - 13 Apr 2023
Cited by 6 | Viewed by 2374
Abstract
An essential function of dockless bikesharing (DBs) is to serve as a feeder mode to the metro. Optimizing the integration between DBs and the metro is of great significance for improving metro travel efficiency. However, the research on DBs–Metro Integration Cycling (DBsMIC) faces [...] Read more.
An essential function of dockless bikesharing (DBs) is to serve as a feeder mode to the metro. Optimizing the integration between DBs and the metro is of great significance for improving metro travel efficiency. However, the research on DBs–Metro Integration Cycling (DBsMIC) faces challenges such as insufficient methods for identification and low identification accuracy. In this study, we improve the enhanced two-step floating catchment area and incorporate Bayes’ rule to propose a method to identify DBsMIC by considering the parameters of time, distance, environmental competition ratio, and POI service power index. Furthermore, an empirical study is conducted in Shenzhen to verify the higher accuracy of the proposed method. Their spatiotemporal behavior pattern is also explored with the help of the kernel density estimation method. The research results will help managers improve the effective redistribution of bicycles, promote the coupling efficiency between transportation modes, and achieve sustainable development of urban transportation. Full article
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45 pages, 61635 KiB  
Article
A Semi-Automatic Semantic-Model-Based Comparison Workflow for Archaeological Features on Roman Ceramics
by Florian Thiery, Jonas Veller, Laura Raddatz, Louise Rokohl, Frank Boochs and Allard W. Mees
ISPRS Int. J. Geo-Inf. 2023, 12(4), 167; https://doi.org/10.3390/ijgi12040167 - 13 Apr 2023
Cited by 2 | Viewed by 3658
Abstract
In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the example of Roman African Red Slip Ware (ARS) and the collection of ARS [...] Read more.
In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the example of Roman African Red Slip Ware (ARS) and the collection of ARS at the LEIZA archaeological research institute. The main challenge is to create a Digital Twin of the ARS objects and artefacts using geometric capturing and semantic modelling of archaeological information. Moreover, the individualisation and comparison of features (appliqués), along with their visualisation, extraction, and rectification, results in a strategy and application for comparison of these features using both geometrical and archaeological aspects with a comprehensible rule set. This method of a semi-automatic semantic model-based comparison workflow for archaeological features on Roman ceramics is showcased, discussed, and concluded in three use cases: woman and boy, human–horse hybrid, and bears with local twists and shifts. Full article
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23 pages, 7231 KiB  
Article
Assessment of Ecosystem Service Value in Response to LULC Changes Using Geospatial Techniques: A Case Study in the Merbil Wetland of the Brahmaputra Valley, Assam, India
by Durlov Lahon, Dhrubajyoti Sahariah, Jatan Debnath, Nityaranjan Nath, Gowhar Meraj, Pankaj Kumar, Shizuka Hashimoto and Majid Farooq
ISPRS Int. J. Geo-Inf. 2023, 12(4), 165; https://doi.org/10.3390/ijgi12040165 - 12 Apr 2023
Cited by 39 | Viewed by 4637
Abstract
The alteration of land use and land cover caused by human activities on a global scale has had a notable impact on ecosystem services at regional and global levels, which are crucial for the survival and welfare of human beings. Merbil, a small [...] Read more.
The alteration of land use and land cover caused by human activities on a global scale has had a notable impact on ecosystem services at regional and global levels, which are crucial for the survival and welfare of human beings. Merbil, a small freshwater wetland located in the Brahmaputra basin in Assam, India, is not exempt from this phenomenon. In the present study, we have estimated and shown a spatio-temporal variation of ecosystem service values in response to land use and land cover alteration for the years 1990, 2000, 2010, and 2021, and predicted the same for 2030 and 2040. Supervised classification and the CA-Markov model were used in this study for land-use and land-cover classification and future projection, respectively. The result showed a significant increase in built-up areas, agricultural land, and aquatic plants and a decrease in open water and vegetation during 1990–2040. The study area experienced a substantial rise in ecosystem service values during the observed period (1990–2021) due to the rapid expansion of built-up areas and agricultural and aquatic land. Although the rise of built-up and agricultural land is economically profitable and has increased the study site’s overall ecosystem service values, decreasing the area under open water and vegetation cover may have led to an ecological imbalance in the study site. Hence, we suggest that protecting the natural ecosystem should be a priority in future land-use planning. The study will aid in developing natural resource sustainability management plans and provide useful guidelines for preserving the local ecological balance in small wetlands over the short to medium term. Full article
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20 pages, 5322 KiB  
Article
SAM-GAN: Supervised Learning-Based Aerial Image-to-Map Translation via Generative Adversarial Networks
by Jian Xu, Xiaowen Zhou, Chaolin Han, Bing Dong and Hongwei Li
ISPRS Int. J. Geo-Inf. 2023, 12(4), 159; https://doi.org/10.3390/ijgi12040159 - 7 Apr 2023
Cited by 9 | Viewed by 4126
Abstract
Accurate translation of aerial imagery to maps is a direction of great value and challenge in mapping, a method of generating maps that does not require using vector data as traditional mapping methods do. The tremendous progress made in recent years in image [...] Read more.
Accurate translation of aerial imagery to maps is a direction of great value and challenge in mapping, a method of generating maps that does not require using vector data as traditional mapping methods do. The tremendous progress made in recent years in image translation based on generative adversarial networks has led to rapid progress in aerial image-to-map translation. Still, the generated results could be better regarding quality, accuracy, and visual impact. This paper proposes a supervised model (SAM-GAN) based on generative adversarial networks (GAN) to improve the performance of aerial image-to-map translation. In the model, we introduce a new generator and multi-scale discriminator. The generator is a conditional GAN model that extracts the content and style space from aerial images and maps and learns to generalize the patterns of aerial image-to-map style transformation. We introduce image style loss and topological consistency loss to improve the model’s pixel-level accuracy and topological performance. Furthermore, using the Maps dataset, a comprehensive qualitative and quantitative comparison is made between the SAM-GAN model and previous methods used for aerial image-to-map translation in combination with excellent evaluation metrics. Experiments showed that SAM-GAN outperformed existing methods in both quantitative and qualitative results. Full article
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16 pages, 1281 KiB  
Article
A Tale of Two Cities: COVID-19 Vaccine Hesitancy as a Result of Racial, Socioeconomic, Digital, and Partisan Divides
by Rui Li, Daniel Erickson, Mareyam Belcaid, Madu Franklin Chinedu and Oluwabukola Olufunke Akanbi
ISPRS Int. J. Geo-Inf. 2023, 12(4), 158; https://doi.org/10.3390/ijgi12040158 - 7 Apr 2023
Viewed by 2510
Abstract
The unprecedented COVID-19 pandemic has drawn great attention to the issue of vaccine hesitancy, as the acceptance of the innovative RNA vaccine is relatively low. Studies have addressed multiple factors, such as socioeconomic, political, and racial backgrounds. These studies, however, rely on survey [...] Read more.
The unprecedented COVID-19 pandemic has drawn great attention to the issue of vaccine hesitancy, as the acceptance of the innovative RNA vaccine is relatively low. Studies have addressed multiple factors, such as socioeconomic, political, and racial backgrounds. These studies, however, rely on survey data from participants as part of the population. This study utilizes the actual data from the U.S. Census Bureau as well as actual 2020 U.S. presidential election results to generate four major category of factors that divide the population: socioeconomic status, race and ethnicity, access to technology, and political identification. This study then selects a region in a traditionally democratic state (Capital Region in New York) and a region in a traditionally republican state (Houston metropolitan area in Texas). Statistical analyses such as correlation and geographically weighted regression reveal that factors such as political identification, education attainment, and non-White Hispanic ethnicity in both regions all impact vaccine acceptance significantly. Other factors, such as poverty and particular minority races, have different influences in each region. These results also highlight the necessity of addressing additional factors to further shed light on vaccine hesitancy and potential solutions according to identified factors. Full article
(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research)
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17 pages, 3871 KiB  
Article
Mapping Imprecision: How to Geocode Data from Inaccurate Historic Maps
by Tomasz Panecki
ISPRS Int. J. Geo-Inf. 2023, 12(4), 149; https://doi.org/10.3390/ijgi12040149 - 2 Apr 2023
Cited by 3 | Viewed by 2899
Abstract
This paper aims to present and discuss the method of geocoding historical place names from historic maps that cannot be georeferenced in the GIS environment. This concerns especially maps drawn in the early modern period, i.e., before the common use of precise topographic [...] Read more.
This paper aims to present and discuss the method of geocoding historical place names from historic maps that cannot be georeferenced in the GIS environment. This concerns especially maps drawn in the early modern period, i.e., before the common use of precise topographic surveys. Such maps are valuable sources of place names and geocoding them is an asset to historical and geographical analyses. Geocoding is a process of matching spatial data (such as place names) with reference datasets (databases, gazetteers) and therefore giving them geographic coordinates. Such referencing can be done using multiple tools (online, desktop), reference datasets (modern, historical) and methods (manual, semi-automatic, automatic), but no suitable approach to handling inaccurate historic maps has yet been proposed. In this paper, selected geocoding strategies were described, as well as the author’s method of matching place names from inaccurate cartographic sources. The study was based on Charles Perthées maps of Polish palatinates (1:225,000, 1783–1804)—maps that are not mathematically precise enough to be georeferenced. The proposed semi-automatic and curated approach results in 85% accuracy. It reflects the manual workflow of historical geographers who identify place names with their modern counterparts by analysing their location and proper name. Full article
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18 pages, 4342 KiB  
Article
Analysis and Visualization of Vessels’ RElative MOtion (REMO)
by Hyowon Ban and Hye-jin Kim
ISPRS Int. J. Geo-Inf. 2023, 12(3), 115; https://doi.org/10.3390/ijgi12030115 - 8 Mar 2023
Cited by 4 | Viewed by 2557
Abstract
This research is a pilot study to develop a maritime traffic control system that supports the decision-making process of control officers, and to evaluate the usability of a prototype tool developed in this study. The study analyzed the movements of multiple vessels through [...] Read more.
This research is a pilot study to develop a maritime traffic control system that supports the decision-making process of control officers, and to evaluate the usability of a prototype tool developed in this study. The study analyzed the movements of multiple vessels through automatic identification system (AIS) data using one of the existing methodologies in GIScience, the RElative MOtion (REMO) approach. The REMO approach in this study measured the relative speed, delta-speed, and the azimuth of each vessel per time unit. The study visualized the results on electronic navigational charts in the prototype tool developed, V-REMO. In addition, the study conducted a user evaluation to assess the user interface (UI) of V-REMO and to future enhance the usability. The general usability of V-REMO, the data visualization, and the readability of information in the UI were tested through in-depth interviews. The results of the user evaluation showed that the users needed changes in the size, position, colors, and transparency of the trajectory symbols in the digital chartmap view of V-REMO for better readability and easier manipulation. The users also indicated a need for multiple color schemes for the spatial data and more landmark information about the study area in the chartmap view. Full article
(This article belongs to the Special Issue Cartography and Geomedia)
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20 pages, 17536 KiB  
Article
Spatial Non-Stationarity of Influencing Factors of China’s County Economic Development Base on a Multiscale Geographically Weighted Regression Model
by Ziwei Huang, Shaoying Li, Yihuan Peng and Feng Gao
ISPRS Int. J. Geo-Inf. 2023, 12(3), 109; https://doi.org/10.3390/ijgi12030109 - 4 Mar 2023
Cited by 12 | Viewed by 4024
Abstract
The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to [...] Read more.
The development of the county economy in China is a complicated process that is influenced by many factors in different ways. This study is based on multi-source big data, such as Tencent user density (TUD) data and point of interest (POI) data, to calculate the different influencing factors, and employed a multiscale geographically weighted regression (MGWR) model to explore their spatial non-stationarity impact on China’s county economic development. The results showed that the multi-source big data can be useful to calculate the influencing factor of China’s county economy because they have a significant correlation with county GDP and have a good models fitting performance. Besides, the MGWR model had prominent advantages over the ordinary least squares (OLS) and geographically weighted regression (GWR) models because it could provide covariate-specific optimized bandwidths to incorporate the spatial scale effect of the independent variables. Moreover, the effects of various factors on the development of the county economy in China exhibited obvious spatial non-stationarity. In particular, the Yangtze River Delta, the Pearl River Delta, and the Beijing-Tianjin-Hebei urban agglomerations showed different characteristics. The findings revealed in this study can furnish a scientific foundation for future regional economic planning in China. Full article
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17 pages, 3127 KiB  
Article
Classification of Spatial Objects with the Use of Graph Neural Networks
by Iwona Kaczmarek, Adam Iwaniak and Aleksandra Świetlicka
ISPRS Int. J. Geo-Inf. 2023, 12(3), 83; https://doi.org/10.3390/ijgi12030083 - 21 Feb 2023
Cited by 3 | Viewed by 3930
Abstract
Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or [...] Read more.
Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topology. In this article, a method for multi-class classification of spatial objects using GNNs is proposed. The method was compared to two others that are based solely on text classification or text classification and an adjacency matrix. The use case for the developed method was the classification of planning zones in local spatial development plans. The experiments indicated that information about the topology of objects has a significant impact on improving the classification results using GNNs. It is also important to take into account different input parameters, such as the document length, the form of the training data representation, or the network architecture used, in order to optimize the model. Full article
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25 pages, 21829 KiB  
Article
BiodivAR: A Cartographic Authoring Tool for the Visualization of Geolocated Media in Augmented Reality
by Julien Mercier, Nicolas Chabloz, Gregory Dozot, Olivier Ertz, Erwan Bocher and Daniel Rappo
ISPRS Int. J. Geo-Inf. 2023, 12(2), 61; https://doi.org/10.3390/ijgi12020061 - 9 Feb 2023
Cited by 9 | Viewed by 3633
Abstract
Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge [...] Read more.
Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge of the context. However, the creation of such experiences typically requires complex programming tools that are beyond the reach of mainstream users. We introduce BiodivAR, a web cartographic tool for the authoring of location-based AR experiences. Developed using a user-centered design methodology and open-source interoperable web technologies, it is the second iteration of an effort that started in 2016. It is designed to meet needs defined through use cases co-designed with end users and enables the creation of custom geolocated points of interest. This approach enabled substantial progress over the previous iteration. Its reliance on geolocation data to anchor augmented objects relative to the user’s position poses a set of challenges: On mobile devices, GNSS accuracy typically lies between 1 m and 30 m. Due to its impact on the anchoring, this lack of accuracy can have deleterious effects on usability. We conducted a comparative user test using the application in combination with two different geolocation data types (GNSS versus RTK). While the test’s results are undergoing analysis, we hereby present a methodology for the assessment of our system’s usability based on the use of eye-tracking devices, geolocated traces and events, and usability questionnaires. Full article
(This article belongs to the Special Issue Cartography and Geomedia)
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20 pages, 8438 KiB  
Article
Estimation of Travel Cost between Geographic Coordinates Using Artificial Neural Network: Potential Application in Vehicle Routing Problems
by Keyju Lee and Junjae Chae
ISPRS Int. J. Geo-Inf. 2023, 12(2), 57; https://doi.org/10.3390/ijgi12020057 - 8 Feb 2023
Cited by 6 | Viewed by 2551
Abstract
The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number [...] Read more.
The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number of locations, restricting large-sized VRPs from being solved in a reasonable amount of time. The time needed to obtain a cost matrix is expensive when routing engines are used, which solve shortest path problems in the back end. In fact, details on the shortest path are redundant; only distance or time values are necessary for VRPs. In this study, an artificial neural network (ANN) that receives two geo-coordinates as input and provides estimated cost (distance and time) as output is trained. The trained ANN model was able to estimate with a mean absolute percentage error of 7.68%, surpassing the quality of 13.2% with a simple regression model on Euclidean distance. The possibility of using a trained model in VRPs is examined with different implementation scenarios. The experimental results with VRPs confirm that using ANN estimation instead of Euclidean distance produces a better solution, which is verified to be statistically significant. The results also suggest that an ANN can be a better choice than routing engines when the trade-off between response time and solution quality is considered. Full article
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17 pages, 69177 KiB  
Article
Crowd Density Estimation and Mapping Method Based on Surveillance Video and GIS
by Xingguo Zhang, Yinping Sun, Qize Li, Xiaodi Li and Xinyu Shi
ISPRS Int. J. Geo-Inf. 2023, 12(2), 56; https://doi.org/10.3390/ijgi12020056 - 8 Feb 2023
Cited by 13 | Viewed by 6139
Abstract
Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation [...] Read more.
Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation model (CSSM) and a crowd denoising model (CDM) suitable for high-altitude scenarios are constructed by transfer learning. Then, based on the homography matrix between the video and remote sensing image, the crowd areas in the video are projected to the map space. Finally, according to the distance from the crowd target to the camera, the camera inclination, and the area of the crowd polygon in the geographic space, a BP neural network for the crowd density estimation is constructed. The results show the following: (1) The test accuracy of the CSSM was 96.70%, and the classification accuracy of the CDM was 86.29%, which can achieve a high-precision crowd extraction in large scenes. (2) The BP neural network for the crowd density estimation was constructed, with an average error of 1.2 and a mean square error of 4.5. Compared to the density map method, the MAE and RMSE of the CDEM-M are reduced by 89.9 and 85.1, respectively, which is more suitable for a high-altitude camera. (3) The crowd polygons were filled with the corresponding number of points, and the symbol was a human icon. The crowd mapping and visual expression were realized. The CDEM-M can be used for crowd supervision in stations, shopping malls, and sports venues. Full article
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20 pages, 18367 KiB  
Article
Revealing the Impact of COVID-19 on Urban Residential Travel Structure Based on Floating Car Trajectory Data: A Case Study of Nantong, China
by Fei Tao, Junjie Wu, Shuang Lin, Yaqiao Lv, Yu Wang and Tong Zhou
ISPRS Int. J. Geo-Inf. 2023, 12(2), 55; https://doi.org/10.3390/ijgi12020055 - 8 Feb 2023
Cited by 6 | Viewed by 2498
Abstract
The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics [...] Read more.
The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics of urban mobility affected by the pandemic but has rarely examined the impact of COVID-19 on the travel conditions and psychological needs of residents. To quantitatively assess travel characteristics during COVID-19, this paper proposed a method by which to determine the purpose of residential travel by combining urban functional areas (UFAs) based on machine learning. Then, the residential travel structure, which includes origin–destination (OD) points, residential travel flow, and the proportion of flows for different purposes, was established. Based on taxi trajectory data obtained during the epidemic in Nantong, China, the case study explores changes in travel flow characteristics under the framework of the residential travel structure. Through comparison of the number and spatial distribution of OD points in the residential travel structure, it is found that residential travel hotspots decreased significantly. The ratios of commuting and medical travel increased from 43.8% to 45.7% and 7.1% to 8.1%, respectively. Conversely, the ratios of other travel types all decreased sharply. Moreover, under Maslow’s hierarchy of needs model, further insights into the impacts of COVID-19 on changes in residential psychological needs are discussed in this paper. This work can provide a reference for decision makers to cope with the change in urban traffic during a public health emergency, which is beneficial to the sustainable healthy development of cities. Full article
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23 pages, 11883 KiB  
Article
Implementation of GIS Tools in the Quality of Life Assessment of Czech Municipalities
by Karel Macků, Jaroslav Burian and Hynek Vodička
ISPRS Int. J. Geo-Inf. 2023, 12(2), 43; https://doi.org/10.3390/ijgi12020043 - 31 Jan 2023
Cited by 3 | Viewed by 3262
Abstract
Although quality of life is a phenomenon with a significant geographical component, its assessment is often only based on non-spatial statistical data. In Czechia, there are currently several assessments of quality of life at the level of municipalities, yet they do not consider [...] Read more.
Although quality of life is a phenomenon with a significant geographical component, its assessment is often only based on non-spatial statistical data. In Czechia, there are currently several assessments of quality of life at the level of municipalities, yet they do not consider the spatial aspect of the input indicators. This study uses the existing quality of life index compiled by the research agencies Median and the Aspen Institute, whose input indicators related to the accessibility of services and facilities have been redesigned to capture real-world phenomena more appropriately with GIS (Geographic Information Systems) tools using network analysis. In accordance with the original methodology, an adjusted index of quality of life was compiled. An update of indicators resulted in a more accurate description of quality of life. The differences between the original and the adjusted index were mainly seen in the areas around the larger cities, where quality of life has significantly risen. On the other hand, rural/rather rural areas experienced a slight decrease in quality of life with the change of inputs. The mapping of the resulting index documents the disparities in quality of life across Czechia and contributes to the discussions on the topic of quality of life in Czechia with new up-to-date reference data. Full article
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15 pages, 2804 KiB  
Article
Imputation of Missing Parts in UAV Orthomosaics Using PlanetScope and Sentinel-2 Data: A Case Study in a Grass-Dominated Area
by Francisco R. da S. Pereira, Aliny A. Dos Reis, Rodrigo G. Freitas, Stanley R. de M. Oliveira, Lucas R. do Amaral, Gleyce K. D. A. Figueiredo, João F. G. Antunes, Rubens A. C. Lamparelli, Edemar Moro and Paulo S. G. Magalhães
ISPRS Int. J. Geo-Inf. 2023, 12(2), 41; https://doi.org/10.3390/ijgi12020041 - 28 Jan 2023
Cited by 2 | Viewed by 2606
Abstract
The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight [...] Read more.
The recent advances in unmanned aerial vehicle (UAV)-based remote sensing systems have broadened the remote sensing applications for agriculture. Despite the great possibilities of using UAVs to monitor agricultural fields, specific problems related to missing parts in UAV orthomosaics due to drone flight restrictions are common in agricultural monitoring, especially in large areas. In this study, we propose a methodological framework to impute missing parts of UAV orthomosaics using PlanetScope (PS) and Sentinel-2 (S2) data and the random forest (RF) algorithm of an integrated crop–livestock system (ICLS) covered by grass at the time. We validated the proposed framework by simulating and imputing artificial missing parts in a UAV orthomosaic and then comparing the original data with the model predictions. Spectral bands and the normalized difference vegetation index (NDVI) derived from PS, as well as S2 images (separately and combined), were used as predictor variables of the UAV spectral bands and NDVI in developing the RF-based imputation models. The proposed framework produces highly accurate results (RMSE = 6.77–17.33%) with a computationally efficient and robust machine-learning algorithm that leverages the wealth of empirical information present in optical satellite imagery (PS and S2) to impute up to 50% of missing parts in a UAV orthomosaic. Full article
(This article belongs to the Special Issue Geomatics in Forestry and Agriculture: New Advances and Perspectives)
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24 pages, 3974 KiB  
Systematic Review
Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things
by Mihai Andronie, George Lăzăroiu, Mariana Iatagan, Iulian Hurloiu, Roxana Ștefănescu, Adrian Dijmărescu and Irina Dijmărescu
ISPRS Int. J. Geo-Inf. 2023, 12(2), 35; https://doi.org/10.3390/ijgi12020035 - 21 Jan 2023
Cited by 100 | Viewed by 9308
Abstract
The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. [...] Read more.
The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms. Full article
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20 pages, 5433 KiB  
Article
Multi-GPU-Parallel and Tile-Based Kernel Density Estimation for Large-Scale Spatial Point Pattern Analysis
by Guiming Zhang and Jin Xu
ISPRS Int. J. Geo-Inf. 2023, 12(2), 31; https://doi.org/10.3390/ijgi12020031 - 18 Jan 2023
Cited by 6 | Viewed by 3495
Abstract
Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one [...] Read more.
Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one GPU for parallel computing. Additionally, it assumes that the input data can be held in GPU memory all at once for computation, which is unrealistic when conducting KDE analysis over large geographic areas at high resolution. This study develops a multi-GPU-parallel and tile-based KDE algorithm to overcome these limitations. It exploits multiple GPUs to speedup complex KDE computation by distributing computation across GPUs, and approaches density estimation with a tile-based strategy to bypass the memory bottleneck. Experiment results show that the parallel KDE algorithm running on multiple GPUs achieves significant speedups over running on a single GPU, and higher speedups are achieved on KDE tasks of a larger problem size. The tile-based strategy renders it feasible to estimate high-resolution density surfaces over large areas even on GPUs with only limited memory. Multi-GPU parallel computing and tile-based density estimation, while incurring very little computational overhead, effectively enable conducting KDE for large-scale spatial point pattern analysis on geospatial big data. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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13 pages, 2209 KiB  
Article
Automatic Clustering of Indoor Area Features in Shopping Malls
by Ziren Gao, Yi Shen, Jingsong Ma, Jie Shen and Jing Zheng
ISPRS Int. J. Geo-Inf. 2023, 12(1), 19; https://doi.org/10.3390/ijgi12010019 - 10 Jan 2023
Viewed by 1988
Abstract
The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map [...] Read more.
The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map area features as generalization objects and deems the automatic clustering of the indoor area features of shopping malls as the research goal. The approach is used to construct an encoder-decoder clustering model, where the encoder consists of a graph convolutional network and its variant models. The results show that the proposed model framework effectively extracts the area features suitable for the indoor space clustering of shopping malls and improves clustering efficacy. Specifically, the model with the Relational Graph Convolutional Network as the encoder demonstrated the best performance, time complexity, and accuracy of clustering results, with accuracy up to 95%. This study extends the research object of cartographic generalization to indoor maps, enabling the automatic clustering of indoor area features, and proposes a clustering model for the important indoor scene of shopping malls. This is valuable for scholars interested in the cartographic generalization of indoor maps. Full article
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23 pages, 5501 KiB  
Article
Diagnosis and Planning Strategies for Quality of Urban Street Space Based on Street View Images
by Jiwu Wang, Yali Hu and Wuxihong Duolihong
ISPRS Int. J. Geo-Inf. 2023, 12(1), 15; https://doi.org/10.3390/ijgi12010015 - 7 Jan 2023
Cited by 8 | Viewed by 4627
Abstract
Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play [...] Read more.
Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play important roles in the high-quality development of urban spatial environments. However, traditional planning design and study methods, typically based on questionnaires, interviews, and on-site research, are inefficient and make it difficult to objectively and comprehensively grasp the overall construction characteristics and problems of urban street space in a large area, thus making it challenging to meet the needs of practical planning. Therefore, based on street view images, this study combined machine learning with an artificial audit to put forward a methodological framework for diagnosing the quality issues of street space. The Gongshu District of Hangzhou, China, was selected as a case study, and the diagnosis of quality problems for streets at different grades was achieved. The diagnosis results showed the current situation and problems of the selected area. Simultaneously, a series of targeted strategies for street spatial update planning was proposed to solve these problems. This diagnostic method, based on a combination of subjective and objective approaches, can be conducive to the precise and comprehensive identification of urban public spatial problems, which is expected to become an effective tool to assist in urban renewal and other planning decisions. Full article
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18 pages, 4374 KiB  
Article
Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division
by Li Cai, Cong Sha, Jing He and Shaowen Yao
ISPRS Int. J. Geo-Inf. 2023, 12(1), 13; https://doi.org/10.3390/ijgi12010013 - 4 Jan 2023
Cited by 3 | Viewed by 2864
Abstract
Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that [...] Read more.
Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing. Full article
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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25 pages, 29480 KiB  
Article
A Fine-Grain Batching-Based Task Allocation Algorithm for Spatial Crowdsourcing
by Yuxin Jiao, Zhikun Lin, Long Yu and Xiaozhu Wu
ISPRS Int. J. Geo-Inf. 2022, 11(3), 203; https://doi.org/10.3390/ijgi11030203 - 17 Mar 2022
Cited by 6 | Viewed by 3440
Abstract
Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it [...] Read more.
Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing. Full article
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17 pages, 2311 KiB  
Article
The Governance of INSPIRE: Evaluating and Exploring Governance Scenarios for the European Spatial Data Infrastructure
by Jaap-Willem Sjoukema, Jalal Samia, Arnold K. Bregt and Joep Crompvoets
ISPRS Int. J. Geo-Inf. 2022, 11(2), 141; https://doi.org/10.3390/ijgi11020141 - 15 Feb 2022
Cited by 5 | Viewed by 4988
Abstract
The development of a European Spatial Data Infrastructure (SDI) officially started with the entry into force of the INSPIRE Directive in 2007. INSPIRE’s implementation phase should be completed by the European Union (EU) and its member states at the end of 2021: a [...] Read more.
The development of a European Spatial Data Infrastructure (SDI) officially started with the entry into force of the INSPIRE Directive in 2007. INSPIRE’s implementation phase should be completed by the European Union (EU) and its member states at the end of 2021: a pivotal point to evaluate INSPIRE’s current governance and explore future scenarios. First, INSPIRE’s governing system is evaluated through an online survey by its involved stakeholders. Second, these results are applied in an agent-based model to explore potential governance scenarios and strategies. The results show that strong aspects of INSPIRE’s governing system are the supported vision and its formal structures, such as standards, technology and roles and responsibilities. Weak aspects are the access to resources, especially budget and time resources, and data use. The agent-based simulations show that INSPIRE is probably more constrained by its budget resources than its current dominant hierarchical interaction mix, although a combination of adaptive governance and continuous budget proved the most sustainable governance scenario. Full article
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17 pages, 64925 KiB  
Article
Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China
by Renfei Yang, Fang Luo, Fu Ren, Wenli Huang, Qianyi Li, Kaixuan Du and Dingdi Yuan
ISPRS Int. J. Geo-Inf. 2022, 11(2), 131; https://doi.org/10.3390/ijgi11020131 - 14 Feb 2022
Cited by 17 | Viewed by 4962
Abstract
Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland [...] Read more.
Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland classification techniques, we proposed an urban wetland identification framework based on an advanced scene-level classification scheme. First, the Sentinel-2 high-resolution multispectral image of Shenzhen was segmented into 320 m × 320 m square patches to generate sample datasets for classification. Next, twelve typical convolutional neural network (CNN) models were transformed for the comparison experiments. Finally, the model with the best performance was used to classify the wetland scenes in Shenzhen, and pattern and composition analyses were also implemented in the classification results. We found that the DenseNet121 model performed best in classifying urban wetland scenes, with overall accuracy (OA) and kappa values reaching 0.89 and 0.86, respectively. The analysis results revealed that the wetland scene in Shenzhen is generally balanced in the east–west direction. Among the wetland scenes, coastal open waters accounted for a relatively high proportion and showed an obvious southward pattern. The remaining swamp, marsh, tidal flat, and pond areas were scattered, accounting for only 4.64% of the total area of Shenzhen. For scattered and dynamic urban wetlands, we are the first to achieve scene-level classification with satisfactory results, thus providing a clearer and easier-to-understand reference for management and protection, which is of great significance for promoting harmony between humanity and ecosystems in cities. Full article
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
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30 pages, 9647 KiB  
Article
Topographic Characteristics of Drainage Divides at the Mountain-Range Scale—A Review of DTM-Based Analytical Tools
by Kacper Jancewicz, Milena Różycka, Mariusz Szymanowski, Maciej Kryza and Piotr Migoń
ISPRS Int. J. Geo-Inf. 2022, 11(2), 116; https://doi.org/10.3390/ijgi11020116 - 6 Feb 2022
Cited by 8 | Viewed by 4399
Abstract
We review DTM-based measures that can be applied to study the main drainage divides of mountain ranges. Both measures proposed in the past and new or modified approaches are presented, in order to show an ensemble of tools and jointly discuss their information [...] Read more.
We review DTM-based measures that can be applied to study the main drainage divides of mountain ranges. Both measures proposed in the past and new or modified approaches are presented, in order to show an ensemble of tools and jointly discuss their information potential and problematic issues. The first group focuses on the main drainage divide (MDD) as a line running along the range and includes elevation profile, sinuosity, and orientation. The second one includes measures used to compare morphometric properties of two parts of the range, located on the opposite sides of the MDD, such as range asymmetry, morphometric properties of drainage basins, and the position of MDD versus maximum elevation within the range. In the third group, morphometric properties of the terrain immediately adjacent to the MDD are considered. These include properties of areas located far beyond the range symmetry line, topographic asymmetry, longitudinal stream profiles, and relief types derived from automatic landform classifications. The majority of these tools supports identification of sectors of the MDD, anomalous in terms of elevation, symmetry of the range, or the geomorphic context. All these measures were applied to the test area of the Sudetes range in Central Europe. Full article
(This article belongs to the Special Issue Geomorphometry and Terrain Analysis)
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24 pages, 4369 KiB  
Article
GisGCN: A Visual Graph-Based Framework to Match Geographical Areas through Time
by Margarita Khokhlova, Nathalie Abadie, Valérie Gouet-Brunet and Liming Chen
ISPRS Int. J. Geo-Inf. 2022, 11(2), 97; https://doi.org/10.3390/ijgi11020097 - 29 Jan 2022
Cited by 1 | Viewed by 4691
Abstract
Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are [...] Read more.
Historical visual sources are particularly useful for reconstructing the successive states of the territory in the past and for analysing its evolution. However, finding visual sources covering a given area within a large mass of archives can be very difficult if they are poorly documented. In the case of aerial photographs, most of the time, this task is carried out by solely relying on the visual content of the images. Convolutional Neural Networks are capable to capture the visual cues of the images and match them to each other given a sufficient amount of training data. However, over time and across seasons, the natural and man-made landscapes may evolve, making historical image-based retrieval a challenging task. We want to approach this cross-time aerial indexing and retrieval problem from a different novel point of view: by using geometrical and topological properties of geographic entities of the researched zone encoded as graph representations which are more robust to appearance changes than the pure image-based ones. Geographic entities in the vertical aerial images are thought of as nodes in a graph, linked to each other by edges representing their spatial relationships. To build such graphs, we propose to use instances from topographic vector databases and state-of-the-art spatial analysis methods. We demonstrate how these geospatial graphs can be successfully matched across time by means of the learned graph embedding. Full article
(This article belongs to the Special Issue Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material)
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19 pages, 539 KiB  
Article
Approaches for the Clustering of Geographic Metadata and the Automatic Detection of Quasi-Spatial Dataset Series
by Javier Lacasta, Francisco Javier Lopez-Pellicer, Javier Zarazaga-Soria, Rubén Béjar and Javier Nogueras-Iso
ISPRS Int. J. Geo-Inf. 2022, 11(2), 87; https://doi.org/10.3390/ijgi11020087 - 26 Jan 2022
Cited by 6 | Viewed by 3660
Abstract
The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset [...] Read more.
The discrete representation of resources in geospatial catalogues affects their information retrieval performance. The performance could be improved by using automatically generated clusters of related resources, which we name quasi-spatial dataset series. This work evaluates whether a clustering process can create quasi-spatial dataset series using only textual information from metadata elements. We assess the combination of different kinds of text cleaning approaches, word and sentence-embeddings representations (Word2Vec, GloVe, FastText, ELMo, Sentence BERT, and Universal Sentence Encoder), and clustering techniques (K-Means, DBSCAN, OPTICS, and agglomerative clustering) for the task. The results demonstrate that combining word-embeddings representations with an agglomerative-based clustering creates better quasi-spatial dataset series than the other approaches. In addition, we have found that the ELMo representation with agglomerative clustering produces good results without any preprocessing step for text cleaning. Full article
(This article belongs to the Special Issue Geospatial Metadata)
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13 pages, 2819 KiB  
Article
Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation
by Giedrė Beconytė, Andrius Balčiūnas, Aurelija Šturaitė and Rita Viliuvienė
ISPRS Int. J. Geo-Inf. 2022, 11(1), 64; https://doi.org/10.3390/ijgi11010064 - 14 Jan 2022
Cited by 5 | Viewed by 4667
Abstract
This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the [...] Read more.
This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas. Full article
(This article belongs to the Special Issue Cartographic Communication of Big Data)
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